R

Ru Core News Md

Developed by spacy
CPU-optimized Russian processing pipeline including token classification, dependency parsing, named entity recognition and other NLP tasks
Downloads 25
Release Time : 3/2/2022

Model Overview

Medium-sized Russian processing model for spaCy, featuring POS tagging, morphological analysis, dependency parsing, named entity recognition, suitable for Russian text processing tasks

Model Features

CPU Optimization
Specifically optimized for CPU processing, suitable for running in environments without GPU
Comprehensive NLP Features
Provides complete NLP processing pipeline from POS tagging to named entity recognition
High-quality Vector Representations
Includes 20,000 unique vectors (300 dimensions), providing good word vector representations

Model Capabilities

POS tagging
Morphological analysis
Lemmatization
Dependency parsing
Named entity recognition
Sentence segmentation

Use Cases

Text Processing
Russian Text Analysis
Performing grammatical and semantic analysis on Russian texts
Accurately identifies POS, morphological features and syntactic relationships
Information Extraction
Extracting named entities from Russian texts
NER F-score reaches 0.9456
Linguistic Research
Russian Grammar Research
Analyzing Russian inflection and syntactic structures
Provides detailed morphological feature annotations
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